[Opt-Net] Post-doctoral position : ''Minimizing Cruising for Parking in City Centers’’ (ESIEE-Paris)

Patrick Siarry siarry at u-pec.fr
Sun Jul 1 08:24:41 CEST 2018


A 10 month post-doctoral position is open at ESIEE-Paris (www.esiee.fr<http://www.esiee.fr/>) University Paris-Est, France, starting October the 1st.

ESIEE is a school for Engineering.
Domains : optimization, big data  analysis and modelling, prediction and real time decision making,
Laboratory : LiSSI, University of Paris-Est.

Location Noisy-le-Grand, 25 minutes to the very center of Paris.
Full time. Net wage 2100 Euros per month.

Contacts :  Arben Cela and René Natowicz {arben.cela, rene.natowicz} @esiee.fr
Please send a CV with list of publications and a short motivation letter.

Posted June 15, 2018.
Closes September 15, 2018.



This post-doctoral research study focuses on commuters cruising for parking, whose impact on traffic congestion is of first importance. In some cities, the time spent for searching a vacant parking spot can amount to 40% the total travel time, due to the limited parking spot availability and the uncoordinated objectives of commuters and suppliers. Hence, commuters’ parking is a challenging issue for transport system planners, operators and regulators.
Getting ever smarter, cities enable real time monitoring, analysis and improvement of citizens’ quality of life. Mobility data that are gathered by different platforms such as smart devices become real-time social observatories. They can help the dynamic modeling of transport infrastructure and facilities. They can help understanding how traffic congestion develops and evolves, unravelling hidden patterns and identifying models that can contribute to efficient traffic management techniques. Furthermore, mobility data make each user an actor as 'smart sensor’ and 'smart controller/decision maker', helping optimizing traffic network infrastructure facilities and improving cities’ mobility and accessibility.
Different control strategies were proposed to regulate the traffic flow through control variables such as variable parking price, variable toll fees and variable commuter departure time. The computation of these control variables generally relies on the observed statistics but do not reflect the actual traffic state at the given time. Hence the main objective of this study is to get profit of real time traffic state information and actual parking occupancy data in order to adjust the dynamic models that were obtained from data statistics, allowing coordinating commuters/drivers objectives so as to find available parking spots, minimizing the overall cruising-for-parking time and improving cities’ mobility and accessibility.
The socio-economic impact of traffic congestion in general, the challenges inherent to the related optimisation framework, and the modeling aspects impose a holistic view of the problem, addressed at several hierarchic levels. The continuous and discrete models and decision variables that are involved in the modeling render this optimization problem a mix integer-continuous one. The nature of the problem asks for modelings that carefully state the granularity levels in order to reach the necessary real time control properties.


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